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ARXIV:2603.16737 · VISUAL REASONING · SUBMITTED 19 MAR · 20:22 UTC · FRESHNESS STALE
ARXIV:2603.16737VISUAL REASONINGSUBMITTED 19 MAR · 20:22 UTCFRESHNESS STALEarXiv
CIRCLES enhances vision-language models by using counterfactual examples for improved in-context learning and causal reasoning.
Opportunity summary
Pain CIRCLES enhances vision-language models by using counterfactual examples for improved in-context learning and causal reasoning.
Evidence 0 refs | 0 sources | 50% coverage
Blocker Evidence unverified
CIRCLES enhances vision-language models by using counterfactual examples for improved in-context learning and causal reasoning. In-context learning (ICL) offers a promising avenue for VLMs to adapt to new tasks, but its effectiveness critically depends…
Vision-language models (VLMs) have achieved impressive performance across a wide range of multimodal reasoning tasks, but they often struggle to disentangle fine-grained visual attributes and reason about underlying causal relationships. In-context learning (ICL) offers…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. By incorporating counterfactual-style examples, CIRCLES enables VLMs to implicitly reason about the causal relations between attributes and outcomes, moving beyond superficial correlations and fostering…
Visual Reasoning moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
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CIRCLES enhances vision-language models by using counterfactual examples for improved in-context learning and causal reasoning.
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10.48550/arXiv.2603.16737CIRCLES enhances vision-language models by using counterfactual examples for improved in-context learning and causal reasoning.
Abstract
Vision-language models (VLMs) have achieved impressive performance across a wide range of multimodal reasoning tasks, but they often struggle to disentangle fine-grained visual attributes and reason about underlying causal relationships. In-context learning (ICL) offers a promising avenue for VLMs to adapt to new tasks, but its effectiveness critically depends on the selection of demonstration examples. Existing retrieval-augmented approaches typically rely on passive similarity-based retrieval, which tends to select correlated but non-causal examples, amplifying spurious associations and limiting model robustness. We introduce CIRCLES (Composed Image Retrieval for Causal Learning Example Selection), a novel framework that actively constructs demonstration sets by retrieving counterfactual-style examples through targeted, attribute-guided composed image retrieval. By incorporating counterfactual-style examples, CIRCLES enables VLMs to implicitly reason about the causal relations between attributes and outcomes, moving beyond superficial correlations and fostering more robust and grounded reasoning. Comprehensive experiments on four diverse datasets demonstrate that CIRCLES consistently outperforms existing methods across multiple architectures, especially on small-scale models, with pronounced gains under information scarcity. Furthermore, CIRCLES retrieves more diverse and causally informative examples, providing qualitative insights into how models leverage in-context demonstrations for improved reasoning. Our code is available at https://github.com/gzxiong/CIRCLES.
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PROBLEM
CIRCLES enhances vision-language models by using counterfactual examples for improved in-context learning and causal reasoning. In-context learning (ICL) offers a promising avenue for VLMs to adapt to new tasks, but its effectiveness critically depends on the selection of demons...
METHOD
Vision-language models (VLMs) have achieved impressive performance across a wide range of multimodal reasoning tasks, but they often struggle to disentangle fine-grained visual attributes and reason about underlying causal relationships. In-context learning (ICL) offers a promis...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. By incorporating counterfactual-style examples, CIRCLES enables VLMs to implicitly reason about the causal relations between attributes and outcomes, moving beyond superficial correlations and fostering m...
WHY NOW
Visual Reasoning moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
Comprehensive experiments on four diverse datasets demonstrate that CIRCLES consistently outperforms existing methods across multiple architectures
Directly stated in abstract with comprehensive experiments mentioned
partial
with pronounced gains under information scarcity
Explicitly mentioned in abstract with specific context
partial
Existing retrieval-augmented approaches typically rely on passive similarity-based retrieval, which tends to select correlated but non-causal examples
Direct statement in abstract describing limitation of existing methods
partial
CIRCLES retrieves more diverse and causally informative examples
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By incorporating counterfactual-style examples, CIRCLES enables VLMs to implicitly reason about the causal relations between attributes and outcomes
Direct claim about mechanism but requires inference about effectiveness
partial
especially on small-scale models
Explicitly mentioned in abstract with specific context
partial
Vision-language models (VLMs) have achieved impressive performance across a wide range of multimodal reasoning tasks, but they often struggle to disentangle fine-grained visual attributes and reason about underlying causal relationships
Direct statement of problem in abstract
partial
CIRCLES (Composed Image Retrieval for Causal Learning Example Selection), a novel framework that actively constructs demonstration sets by retrieving counterfactual-style examples through targeted, attribute-guided composed image retrieval
Direct description of method in abstract
partial
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CIRCLES enhances vision-language models by using counterfactual examples for improved in-context learning and causal reasoning.
Segment
Visual Reasoning
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